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Predictive Analytics 7.5 credits

Prediktiv analys
Second cycle, D7056E
Course syllabus valid: Spring 2021 Sp 3 - Present
The version indicates the term and period for which this course syllabus is valid. The most recent version of the course syllabus is shown first.

Education level
Second cycle
Grade scale
Systems Science
Subject group (SCB)
Informatics/Computer and Systems Sciences
Main field of study
Information Systems Sciences, Mathematics

Entry requirements

In order to meet the general entry requirements for the course, you must have accomplished a minimum of 180 ECTS of university studies, out of which 60 ECTS in the areas of computer or system science. The studies shall have included Introductory Programming (for example D0009E or D0007N) and Fundamentals of Databases (for example D0004N or D0018E). The Data Science Programming course also requires the course D7043E Advanced Data Mining as well as the course D7044E Business Intelligence. Good knowledge in English equivalent to English 6. More information about the English language requirements [].

More information about English language requirements


The selection is based on 20-285 credits

Course Aim

The objective of the course is for the student to develop their knowledge and skills in Predictive Analytics. After passing the course, the student should be able to: 

  1. Explain and use the concepts in predictive analytics
  2. Describe the business situations where & how predictive analytics would, or should, be used.
  3. Explain how predictive analytics is used to address organizational needs
  4. Evaluate a predictive analytics technique
  5. Analyze and reflect on the relationship between its components, current and future 
  6. Plan & execute predictive analytics experiment


The Predictive Analytics course is aimed at providing knowledge to the students on how to make prediction using machine learning techniques. While scientists are accustomed to make predictions based on consolidated and accepted theories, nowadays big data analytics is able to deliver predictions based on executing a sequence of data processing steps. The course explains both the analytics process as well as the techniques for making predictions. The Predictive Analytics course, though, identify some of the key challenges faced, while making predictions. Techniques such as: naïve approach, moving averages, exponential smoothing, trend projection, regression, ANN and deep learning, will be studied in the course with supporting examples and use cases. 


Lectures, labs, assignments, case studies and/or project work. During the course, the students work with individual tasks and/or group work. Some assignments or case studies in the course might contain work in contact with or about the industry. The student uses different methods and techniques, and it is important to choose the right method, technique or computer support for each task. Before and after the tasks are solved, there are lectures to present and discuss different solutions.
Teaching is in English and on the Internet for distance students or on campus for students living here. IT support: Learning management system, e-mail and phone.  The learning management system is used for delivering course material, information and submissions. Knowledge is shared and created within the course through virtual meetings with teachers and other students for discussions, supervision, teamwork and seminars. For students on campus, there will be meetings on campus.  


Through written tests, individual and group/project assignment, different student abilities are examined. Those are: the ability to explain and use prediction techniques and the ability to solve business problems using prediction techniques individually and in groups. 


  Technical Requirements: access to computer with administrative rights, web camera, microphone and Internet connection.  

Ahmed Elragal

Literature. Valid from Spring 2021 Sp 3 (May change until 10 weeks before course start)
Title: Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked examples, and Case Studies
Author: John D. Kelleher, Brian Max Namee, Aoife D'Arcy
Publisher: The MIT Press, 1st edition, 2015

Course offered by
Department of Computer Science, Electrical and Space Engineering

CodeDescriptionGrade scaleHPStatusFrom periodTitle
0001Written exam/Individual examU G VG4.00MandatoryS21
0002Individual taskU G#1.50MandatoryS21
0003Group-/Project workU G#2.00MandatoryS21

Study guidance
Study guidance for the course is to be found in our learning platform Canvas before the course starts. Students applying for single subject courses get more information in the Welcome letter. You will find the learning platform via My LTU.

Syllabus established
by Jonny Johansson, HUL SRT 21 Feb 2020

Last revised
by Jonny Johansson, HUL SRT 06 Nov 2020